# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""A class to store named variables and a scope operator to manage sharing."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections as collections_lib
import copy
import enum  # pylint: disable=g-bad-import-order
import functools
import traceback

import six
from six.moves import xrange  # pylint: disable=redefined-builtin

from tensorflow.python.eager import context
from tensorflow.python.estimator import util as estimator_util
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util import tf_contextlib

__all__ = ["AUTO_REUSE", "VariableScope", "get_variable_scope",
           "get_variable", "get_local_variable", "variable_scope",
           "variable_op_scope", "no_regularizer"]


class _PartitionInfo(object):
  """Holds partition info used by initializer functions.
  """

  def __init__(self, full_shape, var_offset):
    """Constructor.

    Args:
      full_shape: Tuple or list of `int` indicating the full combined shape
        of the partitioned variables.
      var_offset: Tuple or list of `int` specifying offset of this partition
        with respect to the full variable for each dimension.

    Raises:
      TypeError: If `full_shape` or `var_offset` is not a sequence.
      ValueError: If `full_shape` or `var_offset` differ in length. If
        `var_offset` exceeds `full_shape` in any dimension.
    """
    if not isinstance(full_shape, collections_lib.Sequence) or isinstance(
        full_shape, six.string_types):
      raise TypeError(
          "`full_shape` must be a sequence (like tuple or list) instead of " +
          type(full_shape).__name__)

    if not isinstance(var_offset, collections_lib.Sequence) or isinstance(
        var_offset, six.string_types):
      raise TypeError(
          "`var_offset` must be a sequence (like tuple or list) instead of " +
          type(var_offset).__name__)

    if len(var_offset) != len(full_shape):
      raise ValueError(
          "Expected equal length, but `var_offset` is of length {} while "
          "full_shape is of length {}.".format(
              len(var_offset), len(full_shape)))

    for i in xrange(len(full_shape)):
      offset = var_offset[i]
      shape = full_shape[i]
      if offset < 0 or offset >= shape:
        raise ValueError(
            "Expected 0 <= offset < shape but found offset={}, shape={} for "
            "var_offset={}, full_shape={}".format(offset, shape, var_offset,
                                                  full_shape))

    self._full_shape = full_shape
    self._var_offset = var_offset

  @property
  def full_shape(self):
    return self._full_shape

  @property
  def var_offset(self):
    return self._var_offset

  def single_offset(self, shape):
    """Returns the offset when the variable is partitioned in at most one dim.

    Args:
      shape: Tuple or list of `int` indicating the shape of one specific
        variable partition.

    Returns:
      `int` representing the offset in the dimension along which the variable is
       partitioned. Returns 0 if the variable is not being partitioned.

    Raises:
      ValueError: Depending on self.single_slice_dim().
    """

    single_slice_dim = self.single_slice_dim(shape)
    # If this variable is not being partitioned at all, single_slice_dim() could
    # return None.
    if single_slice_dim is None:
      return 0
    return self.var_offset[single_slice_dim]

  def single_slice_dim(self, shape):
    """Returns the slice dim when the variable is partitioned only in one dim.

    Args:
      shape: Tuple or list of `int` indicating the shape of one specific
        variable partition.

    Returns:
      `int` representing the dimension that the variable is partitioned in, or
      `None` if the variable doesn't seem to be partitioned at all.

    Raises:
      TypeError: If `shape` is not a sequence.
      ValueError: If `shape` is not the same length as `self.full_shape`. If
        the variable is partitioned in more than one dimension.
    """
    if not isinstance(shape, collections_lib.Sequence) or isinstance(
        shape, six.string_types):
      raise TypeError(
          "`shape` must be a sequence (like tuple or list) instead of " +
          type(shape).__name__)

    if len(shape) != len(self.full_shape):
      raise ValueError(
          "Expected equal length, but received shape={} of length {} while "
          "self.full_shape={} is of length {}.".format(shape, len(
              shape), self.full_shape, len(self.full_shape)))

    for i in xrange(len(shape)):
      if self.var_offset[i] + shape[i] > self.full_shape[i]:
        raise ValueError(
            "With self.var_offset={}, a partition of shape={} would exceed "
            "self.full_shape={} in dimension {}.".format(
                self.var_offset, shape, self.full_shape, i))

    slice_dim = None
    for i in xrange(len(shape)):
      if shape[i] == self.full_shape[i]:
        continue
      if slice_dim is not None:
        raise ValueError(
            "Cannot use single_slice_dim() with shape={} and "
            "self.full_shape={} since slice dim could be either dimension {} "
            "or {}.".format(shape, self.full_shape, i, slice_dim))
      slice_dim = i

    return slice_dim


class _ReuseMode(enum.Enum):
  """Mode for variable access within a variable scope."""

  # Indicates that variables are to be fetched if they already exist or
  # otherwise created.
  AUTO_REUSE = 1

  # TODO(alive): For TensorFlow 2.0, Deprecate True/False/None API in favor of
  #              enum values.
  # REUSE_FALSE = 2
  # REUSE_TRUE = 3

AUTO_REUSE = _ReuseMode.AUTO_REUSE
AUTO_REUSE.__doc__ = """
When passed in as the value for the `reuse` flag, AUTO_REUSE indicates that
get_variable() should create the requested variable if it doesn't exist or, if
it does exist, simply return it.
"""


class _VariableStore(object):
  """Variable store that carries a number of named Variables.

  New variable names and new variables can be created; all stored
  variables are initialized with the initializer passed to __init__.

  Attributes:
    vars: a dictionary with string names (same as passed in GetVar) as keys
          and the corresponding TensorFlow Variables as values.
  """

  def __init__(self):
    """Create a variable store."""
    self._vars = {}  # A dictionary of the stored TensorFlow variables.
    self._partitioned_vars = {}  # A dict of the stored PartitionedVariables.
    self.variable_scopes_count = {}  # Count re-used variable scopes.

  def open_variable_scope(self, scope_name):
    if scope_name in self.variable_scopes_count:
      self.variable_scopes_count[scope_name] += 1
    else:
      self.variable_scopes_count[scope_name] = 1

  def close_variable_subscopes(self, scope_name):
    for k in self.variable_scopes_count:
      if not scope_name or k.startswith(scope_name + "/"):
        self.variable_scopes_count[k] = 0

  def variable_scope_count(self, scope_name):
    return self.variable_scopes_count.get(scope_name, 0)

  def get_variable(self, name, shape=None, dtype=dtypes.float32,
                   initializer=None, regularizer=None, reuse=None,
                   trainable=True, collections=None, caching_device=None,
                   partitioner=None, validate_shape=True, use_resource=None,
                   custom_getter=None, constraint=None):
    """Gets an existing variable with these parameters or create a new one.

    If a variable with the given name is already stored, we return the stored
    variable. Otherwise, we create a new one.

    Set `reuse` to `True` when you only want to reuse existing Variables.
    Set `reuse` to `False` when you only want to create new Variables.
    Set `reuse` to None (the default) or tf.AUTO_REUSE when you want
    variables to be created if they don't exist or returned if they do.

    If initializer is `None` (the default), the default initializer passed in
    the constructor is used. If that one is `None` too, we use a new
    `glorot_uniform_initializer`. If initializer is a Tensor, we use
    it as a value and derive the shape from the initializer.

    If a partitioner is provided, a `PartitionedVariable` is returned.
    Accessing this object as a `Tensor` returns the shards concatenated along
    the partition axis.

    Some useful partitioners are available.  See, e.g.,
    `variable_axis_size_partitioner` and `min_max_variable_partitioner`.

    Args:
      name: The name of the new or existing variable.
      shape: Shape of the new or existing variable.
      dtype: Type of the new or existing variable (defaults to `DT_FLOAT`).
      initializer: Initializer for the variable.
      regularizer: A (Tensor -> Tensor or None) function; the result of
        applying it on a newly created variable will be added to the collection
        GraphKeys.REGULARIZATION_LOSSES and can be used for regularization.
      reuse: a Boolean, None, or tf.AUTO_REUSE. Controls reuse or creation
        of variables. In Eager mode, this argument is always forced to be
        tf.AUTO_REUSE.
      trainable: If `True` also add the variable to the graph collection
        `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
      collections: List of graph collections keys to add the `Variable` to.
        Defaults to `[GraphKeys.GLOBAL_VARIABLES]` (see `tf.Variable`).
      caching_device: Optional device string or function describing where the
        Variable should be cached for reading.  Defaults to the Variable's
        device.  If not `None`, caches on another device.  Typical use is to
        cache on the device where the Ops using the `Variable` reside, to
        deduplicate copying through `Switch` and other conditional statements.
      partitioner: Optional callable that accepts a fully defined `TensorShape`
        and dtype of the `Variable` to be created, and returns a list of
        partitions for each axis (currently only one axis can be partitioned).
      validate_shape: If False, allows the variable to be initialized with a
        value of unknown shape. If True, the default, the shape of initial_value
        must be known.
      use_resource: If False, creates a regular Variable. If True, creates
        instead an experimental ResourceVariable which has well-defined
        semantics. Defaults to False (will later change to True).
        In Eager mode, this argument is always forced to be true.
      custom_getter: Callable that takes as a first argument the true getter,
        and allows overwriting the internal get_variable method.
        The signature of `custom_getter` should match that of this method,
        but the most future-proof version will allow for changes:
        `def custom_getter(getter, *args, **kwargs)`.  Direct access to
        all `get_variable` parameters is also allowed:
        `def custom_getter(getter, name, *args, **kwargs)`.  A simple identity
        custom getter that simply creates variables with modified names is:
        ```python
        def custom_getter(getter, name, *args, **kwargs):
          return getter(name + '_suffix', *args, **kwargs)
        ```
      constraint: An optional projection function to be applied to the variable
        after being updated by an `Optimizer` (e.g. used to implement norm
        constraints or value constraints for layer weights). The function must
        take as input the unprojected Tensor representing the value of the
        variable and return the Tensor for the projected value
        (which must have the same shape). Constraints are not safe to
        use when doing asynchronous distributed training.

    Returns:
      The created or existing `Variable` (or `PartitionedVariable`, if a
      partitioner was used).

    Raises:
      ValueError: when creating a new variable and shape is not declared,
        when reusing a variable and specifying a conflicting shape,
        or when violating reuse during variable creation.
    """
    if custom_getter is not None and not callable(custom_getter):
      raise ValueError(
          "Passed a custom_getter which is not callable: %s" % custom_getter)

    if context.in_eager_mode():
      reuse = AUTO_REUSE
      use_resource = True

    # If a *_ref type is passed in an error would be triggered further down the
    # stack. We prevent this using base_dtype to get a non-ref version of the
    # type, before doing anything else. When _ref types are removed in favor of
    # resources, this line can be removed.
    try:
      dtype = dtype.base_dtype
    except AttributeError:
      # .base_dtype not existing means that we will try and use the raw dtype
      # which was passed in - this might be a NumPy type which is valid.
      pass

    # This is the main logic of get_variable.  However, custom_getter
    # may override this logic.  So we save it as a callable and pass
    # it to custom_getter.
    # Note: the parameters of _true_getter, and their documentation, match
    # *exactly* item-for-item with the docstring of this method.
    def _true_getter(name, shape=None, dtype=dtypes.float32,  # pylint: disable=missing-docstring
                     initializer=None, regularizer=None, reuse=None,
                     trainable=True, collections=None, caching_device=None,
                     partitioner=None, validate_shape=True, use_resource=None,
                     constraint=None):
      is_scalar = (shape is not None
                   and isinstance(shape, collections_lib.Sequence)
                   and not shape)
      # Partitioned variable case
      if partitioner is not None and not is_scalar:
        if not callable(partitioner):
          raise ValueError(
              "Partitioner must be callable, but received: %s" % partitioner)
        with ops.name_scope(None):
          return self._get_partitioned_variable(name=name,
                                                shape=shape,
                                                dtype=dtype,
                                                initializer=initializer,
                                                regularizer=regularizer,
                                                reuse=reuse,
                                                trainable=trainable,
                                                collections=collections,
                                                caching_device=caching_device,
                                                partitioner=partitioner,
                                                validate_shape=validate_shape,
                                                use_resource=use_resource,
                                                constraint=constraint)

      # Special case for partitioned variable to allow reuse without having to
      # specify partitioner.
      if (reuse is True and partitioner is None
          and name in self._partitioned_vars):
        return self._get_partitioned_variable(name=name,
                                              shape=shape,
                                              dtype=dtype,
                                              initializer=initializer,
                                              regularizer=regularizer,
                                              reuse=reuse,
                                              trainable=trainable,
                                              collections=collections,
                                              caching_device=caching_device,
                                              partitioner=None,
                                              validate_shape=validate_shape,
                                              use_resource=use_resource,
                                              constraint=constraint)

      # Single variable case
      if "%s/part_0" % name in self._vars:
        raise ValueError(
            "No partitioner was provided, but a partitioned version of the "
            "variable was found: %s/part_0. Perhaps a variable of the same "
            "name was already created with partitioning?" % name)

      return self._get_single_variable(
          name=name, shape=shape, dtype=dtype,
          initializer=initializer, regularizer=regularizer, reuse=reuse,
          trainable=trainable, collections=collections,
          caching_device=caching_device, validate_shape=validate_shape,
          use_resource=use_resource, constraint=constraint)

    if custom_getter is not None:
      # Handle backwards compatibility with getter arguments that were added
      # to the API after users started writing custom getters.
      custom_getter_kwargs = {
          "getter": _true_getter,
          "name": name,
          "shape": shape,
          "dtype": dtype,
          "initializer": initializer,
          "regularizer": regularizer,
          "reuse": reuse,
          "trainable": trainable,
          "collections": collections,
          "caching_device": caching_device,
          "partitioner": partitioner,
          "validate_shape": validate_shape,
          "use_resource": use_resource,
      }
      # `fn_args` can handle functions, `functools.partial`, `lambda`.
      if "constraint" in estimator_util.fn_args(custom_getter):
        custom_getter_kwargs["constraint"] = constraint
      return custom_getter(**custom_getter_kwargs)
    else:
      return _true_getter(
          name, shape=shape, dtype=dtype,
          initializer=initializer, regularizer=regularizer,
          reuse=reuse, trainable=trainable, collections=collections,
          caching_device=caching_device, partitioner=partitioner,
          validate_shape=validate_shape, use_resource=use_resource,
          constraint=constraint)

  def _get_partitioned_variable(
      self, name, partitioner, shape=None, dtype=dtypes.float32,
      initializer=None, regularizer=None, reuse=None,
      trainable=True, collections=None, caching_device=None,
      validate_shape=True, use_resource=None, constraint=None):
    """Gets or creates a sharded variable list with these parameters.

    The `partitioner` must be a callable that accepts a fully defined
    `TensorShape` and returns a sequence of integers (the `partitions`).
    These integers describe how to partition the given sharded `Variable`
    along the given dimension.  That is, `partitions[1] = 3` means split
    the `Variable` into 3 shards along dimension 1.  Currently, sharding along
    only one axis is supported.

    If the list of variables with the given name (prefix) is already stored,
    we return the stored variables. Otherwise, we create a new one.

    Set `reuse` to `True` when you only want to reuse existing Variables.
    Set `reuse` to `False` when you only want to create new Variables.
    Set `reuse` to None (the default) or tf.AUTO_REUSE when you want
    variables to be created if they don't exist or returned if they do.

    If initializer is `None` (the default), the default initializer passed in
    the constructor is used. If that one is `None` too, we use a new
    `glorot_uniform_initializer`. If initializer is a Tensor, we use
    it as a value and derive the shape from the initializer.

    If the initializer is a callable, then it will be called for each
    shard.  Otherwise the initializer should match the shape of the entire
    sharded Variable, and it will be sliced accordingly for each shard.

    Some useful partitioners are available.  See, e.g.,
    `variable_axis_size_partitioner` and `min_max_variable_partitioner`.

    Args:
      name: the name of the new or existing sharded variable.
      partitioner: Optional callable that accepts a fully defined `TensorShape`
        and `dtype` of the Variable to be created, and returns a list of
        partitions for each axis (currently only one axis can be partitioned).
      shape: shape of the new or existing sharded variable.
      dtype: type of the new or existing sharded variable
        (defaults to `DT_FLOAT`).
      initializer: initializer for the sharded variable.
      regularizer: a (Tensor -> Tensor or None) function; the result of
        applying it on a newly created variable will be added to the collection
        GraphKeys.REGULARIZATION_LOSSES and can be used for regularization.
      reuse: a Boolean, None, or tf.AUTO_REUSE. Controls reuse or creation
        of variables.
      trainable: If `True` also add the variable to the graph collection
        `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
      collections: List of graph collections keys to add the Variable to.
        Defaults to `[GraphKeys.GLOBAL_VARIABLES]` (see `tf.Variable`).
      caching_device: Optional device string or function describing where the
        Variable should be cached for reading.  Defaults to the Variable's
        device.  If not `None`, caches on another device.  Typical use is to
        cache on the device where the Ops using the Variable reside, to
        deduplicate copying through `Switch` and other conditional statements.
      validate_shape: If False, allows the variable to be initialized with a
        value of unknown shape. If True, the default, the shape of initial_value
        must be known.
      use_resource: If False, creates a regular Variable. If True, creates an
        experimental ResourceVariable which has well-defined semantics. Defaults
        to False (will later change to True).
      constraint: An optional projection function to be applied to the variable
        after being updated by an `Optimizer` (e.g. used to implement norm
        constraints or value constraints for layer weights). The function must
        take as input the unprojected Tensor representing the value of the
        variable and return the Tensor for the projected value
        (which must have the same shape). Constraints are not safe to
        use when doing asynchronous distributed training.

    Returns:
      A `PartitionedVariable` object.

    Raises:
      ValueError: when creating a new variable and shape is not declared,
        when reusing a variable and specifying a conflicting shape,
        when violating reuse during variable creation, or if an existing
        sharded variable exists for the given name but with different sharding.
    """
    if context.in_eager_mode():
      raise NotImplementedError("Partitioned variables are not yet supported "
                                "in Eager mode.")

    initializing_from_value = initializer is not None and isinstance(
        initializer, ops.Tensor)
    reuse_without_partition = reuse and not partitioner

    if name in self._vars:
      raise ValueError(
          "A partitioner was provided, but an unpartitioned version of the "
          "variable was found: %s.  Perhaps a variable of the same name was "
          "already created without partitioning?" % name)

    shape = tensor_shape.as_shape(shape)
    if initializing_from_value:
      shape = shape.merge_with(initializer.get_shape())

    if not reuse_without_partition:
      if not shape.is_fully_defined():
        raise ValueError("Shape of a new partitioned variable (%s) must be "
                         "fully defined, but instead was %s." % (name, shape))

      if shape.ndims < 1:
        raise ValueError("A partitioned Variable must have rank at least 1, "
                         "shape: %s" % shape)

      partitions = partitioner(shape=shape, dtype=dtype)

      if not isinstance(partitions, collections_lib.Sequence):
        raise ValueError("Partitioner must return a sequence, but saw: %s"
                         % partitions)

      if len(partitions) != shape.ndims:
        raise ValueError(
            "Partitioner returned a partition list that does not match the "
            "Variable's rank: %s vs. %s" % (partitions, shape))

      if any([p < 1 for p in partitions]):
        raise ValueError(
            "Partitioner returned zero partitions for some axes: %s" %
            partitions)

    if name in self._partitioned_vars:
      if reuse is False:
        raise ValueError(
            "Partitioned variable with name %s already exists. Did you mean to "
            "set reuse=True or reuse=tf.AUTO_REUSE in VarScope?"
            % name)

      existing_var = self._partitioned_vars[name]
      if not shape.is_compatible_with(existing_var.get_shape()):
        raise ValueError(
            "Trying to reuse partitioned variable %s, but specified shape %s "
            "and found shape %s."
            % (name, shape, existing_var.get_shape()))
      if not dtype.is_compatible_with(existing_var.dtype):
        raise ValueError(
            "Trying to reuse partitioned variable %s, but specified dtype %s "
            "and found dtype %s."
            % (name, dtype.name, existing_var.dtype.name))

      # pylint: disable=protected-access
      if (not reuse_without_partition and
          existing_var._get_partitions() != partitions):
        raise ValueError(
            "Trying to reuse partitioned variable %s, but specified partitions "
            "%s and found partitions %s." %
            (name, partitions, existing_var._get_partitions()))
      # pylint: enable=protected-access

      return existing_var

    if reuse is True:
      raise ValueError("PartitionedVariable %s does not exist, or was not "
                       "created with tf.get_variable(). Did you mean to set "
                       "reuse=None in VarScope?" % name)

    slice_dim, slice_shape = _compute_slice_dim_and_shape(
        shape.as_list(), partitions)

    vs = []
    num_slices = partitions[slice_dim]
    num_slices_with_excess = shape[slice_dim].value % num_slices

    slice_offset = [0] * shape.ndims

    if "%s/part_0" % name in self._vars:
      if "%s/part_%d" % (name, num_slices - 1) not in self._vars:
        raise ValueError(
            "Partitioner returned a different partitioning than what was "
            "already found.  Partitioner returned %d shards, and shard "
            "%s/part_0 was found, but %s/part_%d was not."
            % (num_slices, name, name, num_slices - 1))
      if "%s/part_%d" % (name, num_slices) in self._vars:
        raise ValueError(
            "Partitioner returned a different partitioning than what was "
            "already found.  Partitioner returned %d shards, and shard "
            "%s/part_0 was found, but so was the extra shard %s/part_%d."
            % (num_slices, name, name, num_slices))

    for i in xrange(num_slices):
      var_shape = slice_shape[:]
      var_offset = slice_offset[:]
      partition_info = _PartitionInfo(
          full_shape=shape.as_list(), var_offset=var_offset)
      if i < num_slices_with_excess:
        var_shape[slice_dim] += 1
      slice_offset[slice_dim] += var_shape[slice_dim]

      var_full_name = "%s/part_%d" % (name, i)
      with ops.name_scope(var_full_name + "/PartitionedInitializer"):
        # Create the tensor to initialize the variable with default value.
        if initializer is None:
          init, initializing_from_value = self._get_default_initializer(
              name=name, shape=shape, dtype=dtype)
          if initializing_from_value:
            init_shape = None
          else:
            init_shape = var_shape
        elif callable(initializer):
          init = initializer
          init_shape = var_shape
        elif isinstance(initializer, ops.Tensor):
          init = array_ops.slice(initializer, var_offset, var_shape)
          # Use the dtype of the given tensor.
          dtype = init.dtype.base_dtype
          init_shape = None
        else:
          init = ops.convert_to_tensor(initializer, dtype=dtype)
          init = array_ops.slice(init, var_offset, var_shape)
          init_shape = None

      with ops.name_scope(None):
        var = self._get_single_variable(
            name=var_full_name,
            shape=init_shape,
            dtype=dtype,
            initializer=init,
            partition_info=partition_info,
            regularizer=regularizer,
            reuse=reuse,
            trainable=trainable,
            collections=collections,
            caching_device=caching_device,
            validate_shape=validate_shape,
            use_resource=use_resource,
            constraint=constraint)

      # pylint: disable=protected-access
      var._set_save_slice_info(variables.Variable.SaveSliceInfo(
          name, shape.as_list(), var_offset, var_shape))
      vs.append(var)
      # pylint: enable=protected-access

      # pylint: disable=protected-access
    partitioned_var = variables.PartitionedVariable(name=name,
                                                    shape=shape,
                                                    dtype=dtype,
                                                    variable_list=vs,
                                                    partitions=partitions)
    # pylint: enable=protected-access

    self._partitioned_vars[name] = partitioned_var
    return partitioned_var

  def _get_single_variable(self,
                           name,
                           shape=None,
                           dtype=dtypes.float32,
                           initializer=None,
                           regularizer=None,
                           partition_info=None,
                           reuse=None,
                           trainable=True,
                           collections=None,
                           caching_device=None,
                           validate_shape=True,
                           use_resource=None,
                           constraint=None):
    """Get or create a single Variable (e.g. a shard or entire variable).

    See the documentation of get_variable above (ignore partitioning components)
    for details.

    Args:
      name: see get_variable.
      shape: see get_variable.
      dtype: see get_variable.
      initializer: see get_variable.
      regularizer: see get_variable.
      partition_info: _PartitionInfo object.
      reuse: see get_variable.
      trainable: see get_variable.
      collections: see get_variable.
      caching_device: see get_variable.
      validate_shape: see get_variable.
      use_resource: see get_variable.
      constraint: see get_variable.

    Returns:
      A Variable.  See documentation of get_variable above.

    Raises:
      ValueError: See documentation of get_variable above.
    """
    # Fast-path for get_variable in eager mode when the variable already
    # exists. Note this skips error validation code, so mismatched shapes and
    # dtypes will be caught when the variable is used instead of when the call
    # to get_variable happens.
    if context.in_eager_mode():
      v = self._vars.get(name, None)
      if v is not None:
        return v

    # Set to true if initializer is a constant.
    initializing_from_value = False
    if initializer is not None and not callable(initializer):
      initializing_from_value = True
    if shape is not None and initializing_from_value:
      raise ValueError("If initializer is a constant, do not specify shape.")

    dtype = dtypes.as_dtype(dtype)
    shape = tensor_shape.as_shape(shape)

    if name in self._vars:
      # Here we handle the case when returning an existing variable.
      if reuse is False:
        tb = self._vars[name].op.traceback[::-1]
        # Throw away internal tf entries and only take a few lines.
        tb = [x for x in tb if "tensorflow/python" not in x[0]][:3]
        raise ValueError("Variable %s already exists, disallowed."
                         " Did you mean to set reuse=True or "
                         "reuse=tf.AUTO_REUSE in VarScope? "
                         "Originally defined at:\n\n%s" % (
                             name, "".join(traceback.format_list(tb))))
      found_var = self._vars[name]
      if not shape.is_compatible_with(found_var.get_shape()):
        raise ValueError("Trying to share variable %s, but specified shape %s"
                         " and found shape %s." % (name, shape,
                                                   found_var.get_shape()))
      if not dtype.is_compatible_with(found_var.dtype):
        dtype_str = dtype.name
        found_type_str = found_var.dtype.name
        raise ValueError("Trying to share variable %s, but specified dtype %s"
                         " and found dtype %s." % (name, dtype_str,
                                                   found_type_str))
      return found_var

    # The code below handles only the case of creating a new variable.
    if reuse is True:
      raise ValueError("Variable %s does not exist, or was not created with "
                       "tf.get_variable(). Did you mean to set "
                       "reuse=tf.AUTO_REUSE in VarScope?" % name)
    if not shape.is_fully_defined() and not initializing_from_value:
      raise ValueError("Shape of a new variable (%s) must be fully defined, "
                       "but instead was %s." % (name, shape))

    # Create the tensor to initialize the variable with default value.
    if initializer is None:
      initializer, initializing_from_value = self._get_default_initializer(
          name=name, shape=shape, dtype=dtype)
    # Clear control dependencies while creating the initializer.
    with ops.control_dependencies(None):
      if initializing_from_value:
        init_val = initializer
        variable_dtype = None
      else:
        # Instantiate initializer if provided initializer is a type object.
        if isinstance(initializer, type(init_ops.Initializer)):
          initializer = initializer(dtype=dtype)
        init_val = lambda: initializer(  # pylint: disable=g-long-lambda
            shape.as_list(), dtype=dtype, partition_info=partition_info)
        variable_dtype = dtype.base_dtype

    # Create the variable.
    if use_resource is None:
      # Set the default value if unspecified.
      use_resource = False
    if use_resource:
      v = resource_variable_ops.ResourceVariable(
          initial_value=init_val,
          name=name,
          trainable=trainable,
          collections=collections,
          caching_device=caching_device,
          dtype=variable_dtype,
          validate_shape=validate_shape,
          constraint=constraint)
    else:
      v = variables.Variable(
          initial_value=init_val,
          name=name,
          trainable=trainable,
          collections=collections,
          caching_device=caching_device,
          dtype=variable_dtype,
          validate_shape=validate_shape,
          constraint=constraint)
    self._vars[name] = v
    logging.vlog(1, "Created variable %s with shape %s and init %s", v.name,
                 format(shape), initializer)

    # Run the regularizer if requested and save the resulting loss.
    if regularizer:
      with ops.colocate_with(v):
        with ops.name_scope(name + "/Regularizer/"):
          loss = regularizer(v)
        if loss is not None:
          if context.in_graph_mode():
            v_name = v.name
            loss_name = loss.name
          else:
            v_name = "v_%s" % type(v)
            loss_name = "loss_%s" % type(loss)
          logging.vlog(1, "Applied regularizer to %s and added the result %s "
                       "to REGULARIZATION_LOSSES.", v_name, loss_name)
          ops.add_to_collection(ops.GraphKeys.REGULARIZATION_LOSSES, loss)
    return v

  # Initialize variable when no initializer provided
  def _get_default_initializer(self, name, shape=None, dtype=dtypes.float32):
    """Provide a default initializer and a corresponding value.

    Args:
      name: see get_variable.
      shape: see get_variable.
      dtype: see get_variable.

    Returns:
      initializer and initializing_from_value. See get_variable above.

    Raises:
      ValueError: When giving unsupported dtype.
    """
    # If dtype is DT_FLOAT, provide a uniform unit scaling initializer
    if dtype.is_floating:
      initializer = init_ops.glorot_uniform_initializer()
      initializing_from_value = False
    # If dtype is DT_INT/DT_UINT, provide a default value `zero`
    # If dtype is DT_BOOL, provide a default value `FALSE`
    elif dtype.is_integer or dtype.is_unsigned or dtype.is_bool:
      initializer = init_ops.zeros_initializer()(
          shape=shape, dtype=dtype.base_dtype)
      initializing_from_value = True
    # NOTES:Do we need to support for handling DT_STRING and DT_COMPLEX here?
    else:
      raise ValueError("An initializer for variable %s of %s is required"
                       % (name, dtype.base_dtype))

    return initializer, initializing_from_value


# To stop regularization, use this regularizer
def no_regularizer(_):
  """Use this function to prevent regularization of variables."""
  return None


# TODO(alive): support caching devices and partitioned variables in Eager mode.
class VariableScope(object):
  """Variable scope object to carry defaults to provide to `get_variable`.

  Many of the arguments we need for `get_variable` in a variable store are most
  easily handled with a context. This object is used for the defaults.

  Attributes:
    name: name of the current scope, used as prefix in get_variable.
    initializer: default initializer passed to get_variable.
    regularizer: default regularizer passed to get_variable.
    reuse: Boolean, None, or tf.AUTO_REUSE, setting the reuse in
      get_variable. In Eager mode, this argument is always forced to be
      tf.AUTO_REUSE.
    caching_device: string, callable, or None: the caching device passed to
      get_variable.
    partitioner: callable or `None`: the partitioner passed to `get_variable`.
    custom_getter: default custom getter passed to get_variable.
    name_scope: The name passed to `tf.name_scope`.
    dtype: default type passed to get_variable (defaults to DT_FLOAT).
    use_resource: if False, create a normal Variable; if True create an
      experimental ResourceVariable with well-defined semantics. Defaults
      to False (will later change to True). In Eager mode, this argument is
      always forced to be True.
    constraint: An optional projection function to be applied to the variable
      after being updated by an `Optimizer` (e.g. used to implement norm
      constraints or value constraints for layer weights). The function must
      take as input the unprojected Tensor representing the value of the
      variable and return the Tensor for the projected value
      (which must have the same shape). Constraints are not safe to
      use when doing asynchronous distributed training.
  """

  def __init__(self,
               reuse,
               name="",
               initializer=None,
               regularizer=None,
               caching_device=None,
               partitioner=None,
               custom_getter=None,
               name_scope="",
               dtype=dtypes.float32,
               use_resource=None,
               constraint=None):
    """Creates a new VariableScope with the given properties."""
    self._name = name
    self._initializer = initializer
    self._regularizer = regularizer
    self._reuse = reuse
    self._caching_device = caching_device
    self._partitioner = partitioner
    self._custom_getter = custom_getter
    self._name_scope = name_scope
    self._dtype = dtype
    self._use_resource = use_resource
    self._constraint = constraint
    if context.in_eager_mode():
      if self._caching_device is not None:
        raise NotImplementedError("Caching devices is not yet supported "
                                  "in Eager mode.")
      if self._partitioner is not None:
        raise NotImplementedError("Partitioned variables are not yet supported "
                                  "in Eager mode.")
      self._reuse = AUTO_REUSE
      self._use_resource = True

  @property
  def name(self):
    return self._name

  @property
  def original_name_scope(self):
    return self._name_scope

  @property
  def reuse(self):
    return self._reuse

  @property
  def initializer(self):
    return self._initializer

  @property
  def dtype(self):
    return self._dtype

  @property
  def use_resource(self):
    return self._use_resource

  @property
  def regularizer(self):
    return self._regularizer

  @property
  def caching_device(self):
    return self._caching_device

  @property
  def partitioner(self):
    return self._partitioner

  @property
  def custom_getter(self):
    return self._custom_getter

  @property
  def constraint(self):
    return self._constraint

  def reuse_variables(self):
    """Reuse variables in this scope."""
    self._reuse = True

  def set_initializer(self, initializer):
    """Set initializer for this scope."""
    self._initializer = initializer

  def set_dtype(self, dtype):
    """Set data type for this scope."""
    self._dtype = dtype

  def set_use_resource(self, use_resource):
    """Sets whether to use ResourceVariables for this scope."""
    if context.in_eager_mode() and not use_resource:
      raise ValueError("In eager mode, use_resource cannot be set to false.")
    self._use_resource = use_resource

  def set_regularizer(self, regularizer):
    """Set regularizer for this scope."""
    self._regularizer = regularizer

  def set_caching_device(self, caching_device):
    """Set caching_device for this scope."""
    if context.in_eager_mode():
      raise NotImplementedError("Partitioned variables are not yet supported "
                                "in Eager mode.")
    self._caching_device = caching_device

  def set_partitioner(self, partitioner):
    """Set partitioner for this scope."""
    if partitioner and context.in_eager_mode():
      raise NotImplementedError("Partitioned variables are not yet supported "
                                "in Eager mode.")
    self._partitioner = partitioner

  def set_custom_getter(self, custom_getter):
    """Set custom getter for this scope."""
    self._custom_getter = custom_getter

  def get_collection(self, name):
    """Get this scope's variables."""
    scope = self._name + "/" if self._name else ""
    return ops.get_collection(name, scope)

  def trainable_variables(self):
    """Get this scope's trainable variables."""
    return self.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)

  def global_variables(self):
    """Get this scope's global variables."""
    return self.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)

  def local_variables(self):
    """Get this scope's local variables."""
    return self.get_collection(ops.GraphKeys.LOCAL_VARIABLES)

  def get_variable(self,
                   var_store,
                   name,
                   shape=None,
                   dtype=None,
                   initializer=None,
                   regularizer=None,
                   reuse=None,
                   trainable=True,
                   collections=None,
                   caching_device=None,
                   partitioner=None,
                   validate_shape=True,
                   use_resource=None,
                   custom_getter=None,
                   constraint=None):
    """Gets an existing variable with this name or create a new one."""
    if regularizer is None:
      regularizer = self._regularizer
    if caching_device is None:
      caching_device = self._caching_device
    if partitioner is None:
      partitioner = self._partitioner
    if custom_getter is None:
      custom_getter = self._custom_getter
    if context.in_graph_mode():
      if reuse is None:
        reuse = self._reuse
      if use_resource is None:
        use_resource = self._use_resource
    else:
      reuse = AUTO_REUSE
      use_resource = True

    full_name = self.name + "/" + name if self.name else name
    # Variable names only depend on variable_scope (full_name here),
    # not name_scope, so we reset it below for the time of variable creation.
    with ops.name_scope(None):
      # Check that `initializer` dtype and `dtype` are consistent before
      # replacing them with defaults.
      if (dtype is not None and initializer is not None and
          not callable(initializer)):
        init_dtype = ops.convert_to_tensor(initializer).dtype.base_dtype
        if init_dtype != dtype:
          raise ValueError("Initializer type '%s' and explicit dtype '%s' "
                           "don't match." % (init_dtype, dtype))
      if initializer is None:
        initializer = self._initializer
      if constraint is None:
        constraint = self._constraint
      if dtype is None:
        dtype = self._dtype
      return var_store.get_variable(
          full_name, shape=shape, dtype=dtype, initializer=initializer,
          regularizer=regularizer, reuse=reuse, trainable=trainable,
          collections=collections, caching_device=caching_device,
          partitioner=partitioner, validate_shape=validate_shape,
          use_resource=use_resource, custom_getter=custom_getter,
          constraint=constraint)

  def _get_partitioned_variable(self,
                                var_store,
                                name,
                                shape=None,
                                dtype=None,
                                initializer=None,
                                regularizer=None,
                                trainable=True,
                                collections=None,
                                caching_device=None,
                                partitioner=None,
                                validate_shape=True,
                                use_resource=None,
                                constraint=None):
    """Gets an existing variable with this name or create a new one."""
    if context.in_eager_mode():
      raise NotImplementedError("Partitioned variables are not yet supported "
                                "in Eager mode.")
    if initializer is None:
      initializer = self._initializer
    if regularizer is None:
      regularizer = self._regularizer
    if constraint is None:
      constraint = self._constraint
    if caching_device is None:
      caching_device = self._caching_device
    if partitioner is None:
      partitioner = self._partitioner
    if dtype is None:
      dtype = self._dtype
    if use_resource is None:
      use_resource = self._use_resource

    if self._custom_getter is not None:
      raise ValueError(
          "Private access to _get_partitioned_variable is not allowed when "
          "a custom getter is set.  Current custom getter: %s.  "
          "It is likely that you're using create_partitioned_variables.  "
          "If so, consider instead using get_variable with a non-empty "
          "partitioner parameter instead." % self._custom_getter)

    if partitioner is None:
      raise ValueError("No partitioner was specified")

    # This allows the variable scope name to be used as the variable name if
    # this function is invoked with an empty name arg, for backward
    # compatibility with create_partitioned_variables().
    full_name_list = []
    if self.name:
      full_name_list.append(self.name)
    if name:
      full_name_list.append(name)
    full_name = "/".join(full_name_list)

    # Variable names only depend on variable_scope (full_name here),
    # not name_scope, so we reset it below for the time of variable creation.
    with ops.name_scope(None):
      # pylint: disable=protected-access
      return var_store._get_partitioned_variable(
          full_name, shape=shape, dtype=dtype, initializer=initializer,
          regularizer=regularizer, reuse=self.reuse, trainable=trainable,
          collections=collections, caching_device=caching_device,
          partitioner=partitioner, validate_shape=validate_shape,
          use_resource=use_resource, constraint=constraint)
      # pylint: enable=protected-access


_VARSTORE_KEY = ("__variable_store",)
_VARSCOPE_KEY = ("__varscope",)


def get_variable_scope():
  """Returns the current variable scope."""
  scope = ops.get_collection(_VARSCOPE_KEY)
  if scope:  # This collection has at most 1 element, the default scope at [0].
    return scope[0]
  scope = VariableScope(False)
  ops.add_to_collection(_VARSCOPE_KEY, scope)
  return scope


def _get_default_variable_store():
  store = ops.get_collection(_VARSTORE_KEY)
  if store:
    return store[0]
  store = _VariableStore()
  ops.add_to_collection(_VARSTORE_KEY, store)
  return store


def get_variable(name,
                 shape=None,
                 dtype=None,
                 initializer=None,
                 regularizer=None,
                 trainable=True,
                 collections=None,
                 caching_device=None,
                 partitioner=None,
                 validate_shape=True,
                 use_resource=None,
                 custom_getter=None,
                 constraint=None):
  return get_variable_scope().get_variable(
      _get_default_variable_store(), name, shape=shape, dtype=dtype,
      initializer=initializer, regularizer=regularizer, trainable=trainable,
      collections=collections, caching_device=caching_device,
      partitioner=partitioner, validate_shape=validate_shape,
      use_resource=use_resource, custom_getter=custom_getter,
      constraint=constraint)
get_variable_or_local_docstring = (
    """%s

%sThis function prefixes the name with the current variable scope
and performs reuse checks. See the
@{$variables$Variable Scope How To}
for an extensive description of how reusing works. Here is a basic example:

```python
def foo():
  with tf.variable_scope("foo", reuse=tf.AUTO_REUSE):
    v = tf.get_variable("v", [1])
  return v

v1 = foo()  # Creates v.
v2 = foo()  # Gets the same, existing v.
assert v1 == v2
```

If initializer is `None` (the default), the default initializer passed in
the variable scope will be used. If that one is `None` too, a
`glorot_uniform_initializer` will be used. The initializer can also be
a Tensor, in which case the variable is initialized to this value and shape.

Similarly, if the regularizer is `None` (the default), the default regularizer
passed in the variable scope will be used (if that is `None` too,
then by default no regularization is performed).

If a partitioner is provided, a `PartitionedVariable` is returned.
Accessing this object as a `Tensor` returns the shards concatenated along
the partition axis.

Some useful partitioners are available.  See, e.g.,
`variable_axis_size_partitioner` and `min_max_variable_partitioner`.

Args:
  name: The name of the new or existing variable.
  shape: Shape of the new or existing variable.
  dtype: Type of the new or existing variable (defaults to `DT_FLOAT`).
  initializer: Initializer for the variable if one is created.
  regularizer: A (Tensor -> Tensor or None) function; the result of
    applying it on a newly created variable will be added to the collection
    @{tf.GraphKeys.REGULARIZATION_LOSSES} and can be used for regularization.
  %scollections: List of graph collections keys to add the Variable to.
    Defaults to `[%s]` (see `tf.Variable`).
  caching_device: Optional device string or function describing where the
    Variable should be cached for reading.  Defaults to the Variable's
    device.  If not `None`, caches on another device.  Typical use is to
    cache on the device where the Ops using the Variable reside, to
    deduplicate copying through `Switch` and other conditional statements.
  partitioner: Optional callable that accepts a fully defined `TensorShape`
    and `dtype` of the Variable to be created, and returns a list of
    partitions for each axis (currently only one axis can be partitioned).
  validate_shape: If False, allows the variable to be initialized with a
      value of unknown shape. If True, the default, the shape of initial_value
      must be known.
  use_resource: If False, creates a regular Variable. If true, creates an
    experimental ResourceVariable instead with well-defined semantics.
    Defaults to False (will later change to True). In Eager mode, this argument
    is always forced to be True.
  custom_getter: Callable that takes as a first argument the true getter, and
    allows overwriting the internal get_variable method.
    The signature of `custom_getter` should match that of this method,
    but the most future-proof version will allow for changes:
    `def custom_getter(getter, *args, **kwargs)`.  Direct access to
    all `get_variable` parameters is also allowed:
    `def custom_getter(getter, name, *args, **kwargs)`.  A simple identity
    custom getter that simply creates variables with modified names is:
    ```python
    def custom_getter(getter, name, *args, **kwargs):
      return getter(name + '_suffix', *args, **kwargs)
    ```

Returns:
  The created or existing `Variable` (or `PartitionedVariable`, if a
  partitioner was used).

Raises:
  ValueError: when creating a new variable and shape is not declared,
    when violating reuse during variable creation, or when `initializer` dtype
    and `dtype` don't match. Reuse is set inside `variable_scope`.
""")
get_variable.__doc__ = get_variable_or_local_docstring % (
    "Gets an existing variable with these parameters or create a new one.",
    "",
    "trainable: If `True` also add the variable to the graph collection\n"
    "    `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).\n  ",
    "GraphKeys.GLOBAL_VARIABLES")


@functools.wraps(get_variable)
def get_local_variable(*args, **kwargs):
  kwargs["trainable"] = False
  if "collections" in kwargs:
    kwargs["collections"] += [ops.GraphKeys.LOCAL_VARIABLES]
  else:
    kwargs["collections"] = [ops.GraphKeys.LOCAL_VARIABLES]
  return get_variable(*args, **kwargs)
get_local_variable.__doc__ = get_variable_or_local_docstring % (
    "Gets an existing *local* variable or creates a new one.",
    "Behavior is the same as in `get_variable`, except that variables are\n"
    "added to the `LOCAL_VARIABLES` collection and `trainable` is set to\n"
    "`False`.\n",
    "",
    "GraphKeys.LOCAL_VARIABLES")


def _get_partitioned_variable(name,
                              shape=None,
                              dtype=None,
                              initializer=None,
                              regularizer=None,
                              trainable=True,
                              collections=None,
                              caching_device=None,
                              partitioner=None,
                              validate_shape=True,
                              use_resource=None,
                              constraint=None):
  """Gets or creates a sharded variable list with these parameters.

  The `partitioner` must be a callable that accepts a fully defined
  `TensorShape` and returns a sequence of integers (the `partitions`).
  These integers describe how to partition the given sharded `Variable`
  along the given dimension.  That is, `partitions[1] = 3` means split
  the `Variable` into 3 shards along dimension 1.  Currently, sharding along
  only one axis is supported.

  If the list of variables with the given name (prefix) is already stored,
  we return the stored variables. Otherwise, we create a new one.

  If initializer is `None` (the default), the default initializer passed in
  the constructor is used. If that one is `None` too, we use a new
  `glorot_uniform_initializer`. If initializer is a Tensor, we use
  it as a value and derive the shape from the initializer.

  If the initializer is a callable, then it will be called for each
  shard.  Otherwise the initializer should match the shape of the entire
  sharded Variable, and it will be sliced accordingly for each shard.

  Some useful partitioners are available.  See, e.g.,
  `variable_axis_size_partitioner` and `min_max_variable_partitioner`.

  Args:
    name: The name of the new or existing variable.
    shape: Shape of the new or existing variable.
    dtype: Type of the new or existing variable (defaults to `DT_FLOAT`).
    initializer: Initializer for the variable if one is created.
    regularizer: A (Tensor -> Tensor or None) function; the result of
      applying it on a newly created variable will be added to the collection
      GraphKeys.REGULARIZATION_LOSSES and can be used for regularization.
    trainable: If `True` also add the variable to the graph collection
      `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
    collections: List of graph collections keys to add the Variable to.
      Defaults to `[GraphKeys.GLOBAL_VARIABLES]` (see `tf.Variable`).
    caching_device: Optional device string or function describing where the
      Variable should be cached for reading.  Defaults to the Variable's
      device.  If not `None`, caches on another device.  Typical use is to
      cache on the device where the Ops using the Variable reside, to
      deduplicate copying through `Switch` and other conditional statements.
    partitioner: Optional callable that accepts a fully defined `TensorShape`
      and `dtype` of the Variable to be created, and returns a list of
      partitions for each axis (currently only one axis can be partitioned).
    validate_shape: If False, allows the variable to be initialized with a
        value of unknown shape. If True, the default, the shape of initial_value
        must be known.
    use_resource: If False, creates a regular Variable. If True, creates an
      experimental ResourceVariable instead which has well-defined semantics.
      Defaults to False (will later change to True).
    constraint: An optional projection function to be applied to the variable
      after being updated by an `Optimizer` (e.g. used to implement norm
      constraints or value constraints for layer weights). The function must
      take as input the unprojected Tensor representing the value of the
      variable and return the Tensor for the projected value
      (which must have the same shape). Constraints are not safe to
      use when doing asynchronous distributed training.

  Returns:
    A tuple `(shards, partitions)` where `shards` is the list of `Variable`
    shards and `partitions` is the output of the partitioner on the input
    shape.

  Raises:
    ValueError: when creating a new variable and shape is not declared,
      or when violating reuse during variable creation. Reuse is set inside
      `variable_scope`.
  """
  # pylint: disable=protected-access
  scope = get_variable_scope()
  if scope.custom_getter is not None:
    raise ValueError(
        "Private access to _get_partitioned_variable is not allowed when "
        "a custom getter is set.  Current custom getter: %s.  "
        "It is likely that you're using create_partitioned_variables.  "
        "If so, consider instead using get_variable with a non-empty "
        "partitioner parameter instead." % scope.custom_getter)
  return scope._get_partitioned_variable(
      _get_default_variable_store(), name, shape=shape, dtype=dtype,
      initializer=initializer, regularizer=regularizer, trainable=trainable,
      collections=collections, caching_device=caching_device,
      partitioner=partitioner, validate_shape=validate_shape,
      use_resource=use_resource, constraint=constraint)
  # pylint: enable=protected-access


@tf_contextlib.contextmanager
def _pure_variable_scope(name_or_scope,
                         reuse=None,
                         initializer=None,
                         regularizer=None,
                         caching_device=None,
                         partitioner=None,
                         custom_getter=None,
                         old_name_scope=None,
                         dtype=dtypes.float32,
                         use_resource=None,
                         constraint=None):
  """Creates a context for the variable_scope, see `variable_scope` for docs.

  Note: this does not create a name scope.

  Args:
    name_or_scope: `string` or `VariableScope`: the scope to open.
    reuse: `True` or None, or tf.AUTO_REUSE; if `None`, we inherit the parent
      scope's reuse flag.
    initializer: default initializer for variables within this scope.
    regularizer: default regularizer for variables within this scope.
    caching_device: default caching device for variables within this scope.
    partitioner: default partitioner for variables within this scope.
    custom_getter: default custom getter for variables within this scope.
    old_name_scope: the original name scope when re-entering a variable scope.
    dtype: type of the variables within this scope (defaults to `DT_FLOAT`).
    use_resource: If False, variables in this scope will be regular Variables.
      If True, experimental ResourceVariables will be creates instead, with
      well-defined semantics. Defaults to False (will later change to True).
    constraint: An optional projection function to be applied to the variable
      after being updated by an `Optimizer` (e.g. used to implement norm
      constraints or value constraints for layer weights). The function must
      take as input the unprojected Tensor representing the value of the
      variable and return the Tensor for the projected value
      (which must have the same shape). Constraints are not safe to
      use when doing asynchronous distributed training.

  Yields:
    A scope that can be captured and reused.

  Raises:
    ValueError: when trying to reuse within a create scope, or create within
      a reuse scope, or if reuse is not `None` or `True`.
    TypeError: when the types of some arguments are not appropriate.

  """
  get_variable_scope()  # Ensure that a default exists, then get a pointer.
  # Get the reference to the collection as we want to modify it in place.
  default_varscope = ops.get_collection_ref(_VARSCOPE_KEY)
  old = default_varscope[0]
  var_store = _get_default_variable_store()
  if isinstance(name_or_scope, VariableScope):
    new_name = name_or_scope.name
  else:
    new_name = old.name + "/" + name_or_scope if old.name else name_or_scope
  try:
    var_store.open_variable_scope(new_name)
    if isinstance(name_or_scope, VariableScope):
      old_subscopes = copy.copy(var_store.variable_scopes_count)
      name_scope = name_or_scope._name_scope  # pylint: disable=protected-access
      # Handler for the case when we jump to a shared scope.
      #   We create a new VariableScope (default_varscope[0]) that contains
      #   a copy of the provided shared scope, possibly with changed reuse
      #   and initializer, if the user requested this.
      default_varscope[0] = VariableScope(
          name_or_scope.reuse if not reuse else reuse,
          name=new_name,
          initializer=name_or_scope.initializer,
          regularizer=name_or_scope.regularizer,
          caching_device=name_or_scope.caching_device,
          partitioner=name_or_scope.partitioner,
          dtype=name_or_scope.dtype,
          custom_getter=name_or_scope.custom_getter,
          name_scope=name_scope,
          use_resource=name_or_scope.use_resource,
          constraint=constraint)
      if initializer is not None:
        default_varscope[0].set_initializer(initializer)
      if regularizer is not None:
        default_varscope[0].set_regularizer(regularizer)
      if caching_device is not None:
        default_varscope[0].set_caching_device(caching_device)
      if partitioner is not None:
        default_varscope[0].set_partitioner(partitioner)
      if custom_getter is not None:
        default_varscope[0].set_custom_getter(
            _maybe_wrap_custom_getter(
                custom_getter, name_or_scope.custom_getter))
      if dtype is not None:
        default_varscope[0].set_dtype(dtype)
      if use_resource is not None:
        default_varscope[0].set_use_resource(use_resource)
      yield default_varscope[0]
    else:
      # Handler for the case when we just prolong current variable scope.
      #   VariableScope with name extended by the provided one, and inherited
      #   reuse and initializer (except if the user provided values to set).
      reuse = reuse or old.reuse  # Re-using is inherited by sub-scopes.
      default_varscope[0] = VariableScope(
          reuse,
          name=new_name,
          initializer=old.initializer,
          regularizer=old.regularizer,
          caching_device=old.caching_device,
          partitioner=old.partitioner,
          dtype=old.dtype,
          use_resource=old.use_resource,
          custom_getter=old.custom_getter,
          name_scope=old_name_scope or name_or_scope,
          constraint=constraint)
      if initializer is not None:
        default_varscope[0].set_initializer(initializer)
      if regularizer is not None:
        default_varscope[0].set_regularizer(regularizer)
      if caching_device is not None:
        default_varscope[0].set_caching_device(caching_device)
      if partitioner is not None:
        default_varscope[0].set_partitioner(partitioner)
      if custom_getter is not None:
        default_varscope[0].set_custom_getter(
            _maybe_wrap_custom_getter(custom_getter, old.custom_getter))
      if dtype is not None:
        default_varscope[0].set_dtype(dtype)
      if use_resource is not None:
        default_varscope[0].set_use_resource(use_resource)
      yield default_varscope[0]
  finally:
    var_store.close_variable_subscopes(new_name)
    # If jumping out from a non-prolonged scope, restore counts.
    if isinstance(name_or_scope, VariableScope):
      var_store.variable_scopes_count = old_subscopes
    default_varscope[0] = old


def _maybe_wrap_custom_getter(custom_getter, old_getter):
  """Wrap a call to a custom_getter to use the old_getter internally."""
  if old_getter is None:
    return custom_getter

  # The new custom_getter should call the old one
  def wrapped_custom_getter(getter, *args, **kwargs):
    # Call:
    #  custom_getter(
    #    lambda: old_getter(true_getter, ...), *args, **kwargs)
    # which means custom_getter will call old_getter, which
    # will call the true_getter, perform any intermediate
    # processing, and return the results to the current
    # getter, which will also perform additional processing.
    return custom_getter(
        functools.partial(old_getter, getter),
        *args, **kwargs)
  return wrapped_custom_getter


def _get_unique_variable_scope(prefix):
  """Get a name with the given prefix unique in the current variable scope."""
  var_store = _get_default_variable_store()
  current_scope = get_variable_scope()
  name = current_scope.name + "/" + prefix if current_scope.name else prefix
  if var_store.variable_scope_count(name) == 0:
    return prefix
  idx = 1
  while var_store.variable_scope_count(name + ("_%d" % idx)) > 0:
    idx += 1
  return prefix + ("_%d" % idx)


# pylint: disable=g-doc-return-or-yield
@tf_contextlib.contextmanager
def variable_scope(name_or_scope,
                   default_name=None,
                   values=None,
                   initializer=None,
                   regularizer=None,
                   caching_device=None,
                   partitioner=None,
                   custom_getter=None,
                   reuse=None,
                   dtype=None,
                   use_resource=None,
                   constraint=None):
  """Returns a context manager for defining ops that creates variables (layers).

  This context manager validates that the (optional) `values` are from
  the same graph, ensures that graph is the default graph, and pushes a
  name scope and a variable scope.

  If `name_or_scope` is not None, it is used as is. If `scope` is None, then
  `default_name` is used.  In that case, if the same name has been previously
  used in the same scope, it will made unique be appending `_N` to it.

  Variable scope allows to create new variables and to share already created
  ones while providing checks to not create or share by accident. For details,
  see the @{$variables$Variable Scope How To},
  here we present only a few basic examples.

  Simple example of how to create a new variable:

  ```python
  with tf.variable_scope("foo"):
      with tf.variable_scope("bar"):
          v = tf.get_variable("v", [1])
          assert v.name == "foo/bar/v:0"
  ```

  Basic example of sharing a variable AUTO_REUSE:

  ```python
  def foo():
    with tf.variable_scope("foo", reuse=tf.AUTO_REUSE):
      v = tf.get_variable("v", [1])
    return v

  v1 = foo()  # Creates v.
  v2 = foo()  # Gets the same, existing v.
  assert v1 == v2


  Basic example of sharing a variable with reuse=True:

  ```python
  with tf.variable_scope("foo"):
      v = tf.get_variable("v", [1])
  with tf.variable_scope("foo", reuse=True):
      v1 = tf.get_variable("v", [1])
  assert v1 == v
  ```

  Sharing a variable by capturing a scope and setting reuse:

  ```python
  with tf.variable_scope("foo") as scope:
      v = tf.get_variable("v", [1])
      scope.reuse_variables()
      v1 = tf.get_variable("v", [1])
  assert v1 == v
  ```

  To prevent accidental sharing of variables, we raise an exception when
  getting an existing variable in a non-reusing scope.

  ```python
  with tf.variable_scope("foo"):
      v = tf.get_variable("v", [1])
      v1 = tf.get_variable("v", [1])
      #  Raises ValueError("... v already exists ...").
  ```

  Similarly, we raise an exception when trying to get a variable that
  does not exist in reuse mode.

  ```python
  with tf.variable_scope("foo", reuse=True):
      v = tf.get_variable("v", [1])
      #  Raises ValueError("... v does not exists ...").
  ```

  Note that the `reuse` flag is inherited: if we open a reusing scope,
  then all its sub-scopes become reusing as well.

  A note about name scoping: Setting `reuse` does not impact the naming of other
  ops such as mult. See related discussion on [github#6189](https://github.com/tensorflow/tensorflow/issues/6189)

  Note that up to and including version 1.0, it was allowed (though
  explicitly discouraged) to pass False to the reuse argument, yielding
  undocumented behaviour slightly different from None. Starting at 1.1.0
  passing None and False as reuse has exactly the same effect.

  Args:
    name_or_scope: `string` or `VariableScope`: the scope to open.
    default_name: The default name to use if the `name_or_scope` argument is
      `None`, this name will be uniquified. If name_or_scope is provided it
      won't be used and therefore it is not required and can be None.
    values: The list of `Tensor` arguments that are passed to the op function.
    initializer: default initializer for variables within this scope.
    regularizer: default regularizer for variables within this scope.
    caching_device: default caching device for variables within this scope.
    partitioner: default partitioner for variables within this scope.
    custom_getter: default custom getter for variables within this scope.
    reuse: `True`, None, or tf.AUTO_REUSE; if `True`, we go into reuse mode
      for this scope as well as all sub-scopes; if tf.AUTO_REUSE, we create
      variables if they do not exist, and return them otherwise; if None, we
      inherit the parent scope's reuse flag. In Eager mode, this argument is
      always forced to be tf.AUTO_REUSE.
    dtype: type of variables created in this scope (defaults to the type
      in the passed scope, or inherited from parent scope).
    use_resource: If False, all variables will be regular Variables. If True,
      experimental ResourceVariables with well-defined semantics will be used
      instead. Defaults to False (will later change to True). In Eager mode,
      this argument is always forced to be True.
    constraint: An optional projection function to be applied to the variable
      after being updated by an `Optimizer` (e.g. used to implement norm
      constraints or value constraints for layer weights). The function must
      take as input the unprojected Tensor representing the value of the
      variable and return the Tensor for the projected value
      (which must have the same shape). Constraints are not safe to
      use when doing asynchronous distributed training.

  Returns:
    A scope that can be captured and reused.

  Raises:
    ValueError: when trying to reuse within a create scope, or create within
      a reuse scope.
    TypeError: when the types of some arguments are not appropriate.
  """
  if default_name is None and name_or_scope is None:
    raise TypeError("If default_name is None then name_or_scope is required")
  if reuse is False:  # We don't allow non-inheriting scopes, False = None here.
    reuse = None
  if not (reuse is True or reuse is None or reuse is AUTO_REUSE):
    raise ValueError("The reuse parameter must be True or False or None.")
  if values is None:
    values = []
  g = ops._get_graph_from_inputs(values)  # pylint: disable=protected-access
  with g.as_default():
    if name_or_scope is not None:
      if not isinstance(name_or_scope, (VariableScope,) + six.string_types):
        raise TypeError("VariableScope: name_or_scope must be a string or "
                        "VariableScope.")
      if isinstance(name_or_scope, six.string_types):
        name_scope = name_or_scope
      else:
        name_scope = name_or_scope.name.split("/")[-1]
      if name_scope:
        with ops.name_scope(name_scope) as cur_name_scope:
          if isinstance(name_or_scope, six.string_types):
            old_name_scope = cur_name_scope
          else:
            old_name_scope = name_or_scope.original_name_scope
          with _pure_variable_scope(
              name_or_scope,
              reuse=reuse,
              initializer=initializer,
              regularizer=regularizer,
              caching_device=caching_device,
              partitioner=partitioner,
              custom_getter=custom_getter,
              old_name_scope=old_name_scope,
              dtype=dtype,
              use_resource=use_resource,
              constraint=constraint) as vs:
            yield vs
      else:
        # This can only happen if someone is entering the root variable scope.
        with _pure_variable_scope(
            name_or_scope,
            reuse=reuse,
            initializer=initializer,
            regularizer=regularizer,
            caching_device=caching_device,
            partitioner=partitioner,
            custom_getter=custom_getter,
            dtype=dtype,
            use_resource=use_resource,
            constraint=constraint) as vs:
          yield vs
    else:  # Here name_or_scope is None. Using default name, but made unique.
      if reuse:
        raise ValueError("reuse=True cannot be used without a name_or_scope")
      with ops.name_scope(default_name) as scope:
        unique_default_name = _get_unique_variable_scope(default_name)
        with _pure_variable_scope(
            unique_default_name,
            initializer=initializer,
            regularizer=regularizer,
            caching_device=caching_device,
            partitioner=partitioner,
            custom_getter=custom_getter,
            old_name_scope=scope,
            dtype=dtype,
            use_resource=use_resource,
            constraint=constraint) as vs:
          yield vs


# pylint: disable=g-doc-return-or-yield
@tf_contextlib.contextmanager
def variable_op_scope(values,
                      name_or_scope,
                      default_name=None,
                      initializer=None,
                      regularizer=None,
                      caching_device=None,
                      partitioner=None,
                      custom_getter=None,
                      reuse=None,
                      dtype=None,
                      use_resource=None,
                      constraint=None):
  """Deprecated: context manager for defining an op that creates variables."""
  logging.warn("tf.variable_op_scope(values, name, default_name) is deprecated,"
               " use tf.variable_scope(name, default_name, values)")
  with variable_scope(name_or_scope,
                      default_name=default_name,
                      values=values,
                      initializer=initializer,
                      regularizer=regularizer,
                      caching_device=caching_device,
                      partitioner=partitioner,
                      custom_getter=custom_getter,
                      reuse=reuse,
                      dtype=dtype,
                      use_resource=use_resource,
                      constraint=constraint) as scope:
    yield scope


def _compute_slice_dim_and_shape(full_shape, slicing):
  """Computes which dimension is being sliced and the typical slice shape."""

  slice_shape = [0] * len(full_shape)
  slice_dim = None
  for dim, num_slices in enumerate(slicing):
    dim_size = full_shape[dim]
    if num_slices <= 0 or dim_size < num_slices:
      raise ValueError("Cannot create %d slices for size %d. shape: %s, "
                       "slicing: %s" %
                       (num_slices, full_shape[dim], full_shape, slicing))
    if num_slices == 1:
      # Not slicing in this dimension.
      slice_shape[dim] = dim_size
    elif slice_dim is not None:
      # We only support slicing along one of the dimensions.
      raise ValueError("Can only slice a variable along one dimension: "
                       "shape: %s, slicing: %s" % (full_shape, slicing))
    else:
      # Note: We will add any extras onto the last slice, later.
      slice_dim = dim
      slice_shape[dim] = dim_size // num_slices

  # Degenerate case: If "slicing" was all ones, pretend we are slicing along
  # the first dimension.
  if slice_dim is None:
    slice_dim = 0
  return slice_dim, slice_shape


def variable(initial_value=None,
             trainable=True,
             collections=None,
             validate_shape=True,
             caching_device=None,
             name=None,
             dtype=None):
  if get_variable_scope().use_resource:
    return resource_variable_ops.ResourceVariable(
        initial_value=initial_value, trainable=trainable,
        collections=collections, validate_shape=validate_shape,
        caching_device=caching_device, name=name, dtype=dtype)
  else:
    return variables.Variable(
        initial_value=initial_value, trainable=trainable,
        collections=collections, validate_shape=validate_shape,
        caching_device=caching_device, name=name, dtype=dtype)
